Numpy mask based on if a value is in some other list

Question

I have searched high and low and just cant find a way to do it (Its possible i was searching for the wrong terms.)

I would like to create a mask (eg [True False False True True]) based on whether each value is in some other list.

a=np.array([11,12,13,14,15,16,17])
mask= a in [14,16,8] #(this doesnt work at all!)
#I would like to see [False False False True False True False]

so far the best i can come up with is a list comprehension

mask = [True if x in other_list else False for x in my_numpy_array]

please let me know if you know of some secret sauce to do this with numpy and fast (Computationally), as this list in reality is huge...

Answer 1

Use numpy.in1d():

In [6]: np.in1d(a, [14, 16, 18])
Out[6]: array([False, False, False,  True, False,  True, False], dtype=bool)

Answer 2

Accepted answer is right but currently numpy's docs recommend using isin function instead of in1d